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loleg/Abalone-ML-DataPackage-example.ipynb

Created January 28, 2019 15:58
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An example of using DataHub and the Frictionless Data libraries with the Keras library for Machine Learning
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Abalone-ML-DataPackage-example.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Package with ML example\n",
"\n",
"This is an example notebook for an [AMLD 2019 workshop](https://www.appliedmldays.org/workshops/industrial-open-data). Slides and additional notes are [available here](https://forum.schoolofdata.ch/t/27-1-shipshape-open-data-amld-2019/491/1).\n",
"\n",
"We use a classic Machine Learning dataset, [Abalone](https://datahub.io/machine-learning/abalone#python), based on research made [in the 90's](http://archive.ics.uci.edu/ml/datasets/Abalone) - and now hosted in the [Machine Learning collection](https://datahub.io/machine-learning/) on DataHub.\n",
"\n",
"The basic ML test is copied with very slight modifications from a [blog post](http://ericstrong.org/abalone-with-keras-part-1/) by Eric Strong, whose sources are also [on GitHub](https://github.com/drericstrong/Blog/blob/master/20170304_AbaloneWithKerasPart1.ipynb). We also add some in-line visuals. \n",
"\n",
"We also strongly encourage the reader to check out this dataset at [OpenML](https://www.openml.org), which conveniently summarizes studies and provides comparative reports like [this one](https://www.openml.org/t/2075) for a given dataset like [abalone](https://www.openml.org/d/183). OpenML [supports Frictionless Data](https://frictionlessdata.io/articles/openml/) and can help you integrate and scale your research."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Using TensorFlow backend.\n"
]
}
],
"source": [
"from datapackage import Package\n",
"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense\n",
"import keras\n",
"\n",
"import pandas as pd\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"from vega import VegaLite "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"After the Package is loaded from DataHub with the handy one-liner below, we print list of all resources, to make sure we're loaded and ready."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['validation_report', 'abalone_csv', 'abalone_json', 'abalone_zip', 'abalone_csv_preview', 'abalone_arff', 'abalone']\n"
]
}
],
"source": [
"package = Package('https://datahub.io/machine-learning/abalone/datapackage.json')\n",
"print(package.resource_names)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can pick the CSV formatted file using `get_resource`. The next command checks foreign keys and raises an exception if there are integrity issues."
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abalone = package.get_resource('abalone_csv')\n",
"abalone.check_relations()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abalone.tabular"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[['M',\n",
" Decimal('0.455'),\n",
" Decimal('0.365'),\n",
" Decimal('0.095'),\n",
" Decimal('0.514'),\n",
" Decimal('0.2245'),\n",
" Decimal('0.101'),\n",
" Decimal('0.15'),\n",
" Decimal('15')]]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abalone.read(limit=1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We can see that the Data Package library is able to fetch the resource and it's data, do a basic consistency check according to the [Table Schema](https://frictionlessdata.io/specs/table-schema/). Instead of using the data diretly in the form of arrays, it's more convenient to load it into [Pandas](https://pandas.pydata.org/). We can then assign our own names to the columns and use convenience functions."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Sex',\n",
" 'Length',\n",
" 'Diameter',\n",
" 'Height',\n",
" 'Whole_weight',\n",
" 'Shucked_weight',\n",
" 'Viscera_weight',\n",
" 'Shell_weight',\n",
" 'Class_number_of_rings']"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abalone_df = pd.read_csv(abalone.descriptor['path'])\n",
"abalone.headers"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Sex</th>\n",
" <th>Length</th>\n",
" <th>Diameter</th>\n",
" <th>Height</th>\n",
" <th>Whole_weight</th>\n",
" <th>Shucked_weight</th>\n",
" <th>Viscera_weight</th>\n",
" <th>Shell_weight</th>\n",
" <th>Rings</th>\n",
" <th>Male</th>\n",
" <th>Female</th>\n",
" <th>Infant</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>M</td>\n",
" <td>0.455</td>\n",
" <td>0.365</td>\n",
" <td>0.095</td>\n",
" <td>0.5140</td>\n",
" <td>0.2245</td>\n",
" <td>0.1010</td>\n",
" <td>0.150</td>\n",
" <td>15</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>M</td>\n",
" <td>0.350</td>\n",
" <td>0.265</td>\n",
" <td>0.090</td>\n",
" <td>0.2255</td>\n",
" <td>0.0995</td>\n",
" <td>0.0485</td>\n",
" <td>0.070</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>F</td>\n",
" <td>0.530</td>\n",
" <td>0.420</td>\n",
" <td>0.135</td>\n",
" <td>0.6770</td>\n",
" <td>0.2565</td>\n",
" <td>0.1415</td>\n",
" <td>0.210</td>\n",
" <td>9</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>M</td>\n",
" <td>0.440</td>\n",
" <td>0.365</td>\n",
" <td>0.125</td>\n",
" <td>0.5160</td>\n",
" <td>0.2155</td>\n",
" <td>0.1140</td>\n",
" <td>0.155</td>\n",
" <td>10</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>I</td>\n",
" <td>0.330</td>\n",
" <td>0.255</td>\n",
" <td>0.080</td>\n",
" <td>0.2050</td>\n",
" <td>0.0895</td>\n",
" <td>0.0395</td>\n",
" <td>0.055</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Sex Length Diameter Height Whole_weight Shucked_weight Viscera_weight \\\n",
"0 M 0.455 0.365 0.095 0.5140 0.2245 0.1010 \n",
"1 M 0.350 0.265 0.090 0.2255 0.0995 0.0485 \n",
"2 F 0.530 0.420 0.135 0.6770 0.2565 0.1415 \n",
"3 M 0.440 0.365 0.125 0.5160 0.2155 0.1140 \n",
"4 I 0.330 0.255 0.080 0.2050 0.0895 0.0395 \n",
"\n",
" Shell_weight Rings Male Female Infant \n",
"0 0.150 15 1 0 0 \n",
"1 0.070 7 1 0 0 \n",
"2 0.210 9 0 1 0 \n",
"3 0.155 10 1 0 0 \n",
"4 0.055 7 0 0 1 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"abalone_df.rename(inplace=True, columns={\"Class_number_of_rings\": \"Rings\"})\n",
"abalone_df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's do a quick plot as well using [iPyVega](https://github.com/vega/ipyvega):"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"application/javascript": [
"var spec = {\"$schema\": \"https://vega.github.io/schema/vega-lite/v3.json\", \"mark\": \"point\", \"encoding\": {\"y\": {\"type\": \"quantitative\", \"field\": \"Rings\"}, \"x\": {\"type\": \"quantitative\", \"field\": \"Length\"}}, \"data\": {\"values\": [{\"Sex\": \"M\", \"Length\": 0.455, \"Diameter\": 0.365, \"Height\": 0.095, \"Whole_weight\": 0.514, \"Shucked_weight\": 0.2245, \"Viscera_weight\": 0.10099999999999999, \"Shell_weight\": 0.15, \"Rings\": 15, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.35, \"Diameter\": 0.265, \"Height\": 0.09, \"Whole_weight\": 0.2255, \"Shucked_weight\": 0.0995, \"Viscera_weight\": 0.0485, \"Shell_weight\": 0.07, \"Rings\": 7, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.53, \"Diameter\": 0.42, \"Height\": 0.135, \"Whole_weight\": 0.677, \"Shucked_weight\": 0.2565, \"Viscera_weight\": 0.1415, \"Shell_weight\": 0.21, \"Rings\": 9, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.44, \"Diameter\": 0.365, \"Height\": 0.125, \"Whole_weight\": 0.516, \"Shucked_weight\": 0.2155, \"Viscera_weight\": 0.114, \"Shell_weight\": 0.155, \"Rings\": 10, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"I\", \"Length\": 0.33, \"Diameter\": 0.255, \"Height\": 0.08, \"Whole_weight\": 0.205, \"Shucked_weight\": 0.0895, \"Viscera_weight\": 0.0395, \"Shell_weight\": 0.055, \"Rings\": 7, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"I\", \"Length\": 0.425, \"Diameter\": 0.3, \"Height\": 0.095, \"Whole_weight\": 0.3515, \"Shucked_weight\": 0.141, \"Viscera_weight\": 0.0775, \"Shell_weight\": 0.12, \"Rings\": 8, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"F\", \"Length\": 0.53, \"Diameter\": 0.415, \"Height\": 0.15, \"Whole_weight\": 0.7775, \"Shucked_weight\": 0.237, \"Viscera_weight\": 0.1415, \"Shell_weight\": 0.33, \"Rings\": 20, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.545, \"Diameter\": 0.425, \"Height\": 0.125, \"Whole_weight\": 0.768, \"Shucked_weight\": 0.294, \"Viscera_weight\": 0.1495, \"Shell_weight\": 0.26, \"Rings\": 16, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.475, \"Diameter\": 0.37, \"Height\": 0.125, \"Whole_weight\": 0.5095, \"Shucked_weight\": 0.2165, \"Viscera_weight\": 0.1125, \"Shell_weight\": 0.165, \"Rings\": 9, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.55, \"Diameter\": 0.44, \"Height\": 0.15, \"Whole_weight\": 0.8945, \"Shucked_weight\": 0.3145, \"Viscera_weight\": 0.151, \"Shell_weight\": 0.32, \"Rings\": 19, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.525, \"Diameter\": 0.38, \"Height\": 0.14, \"Whole_weight\": 0.6065, \"Shucked_weight\": 0.19399999999999998, \"Viscera_weight\": 0.1475, \"Shell_weight\": 0.21, \"Rings\": 14, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.43, \"Diameter\": 0.35, \"Height\": 0.11, \"Whole_weight\": 0.406, \"Shucked_weight\": 0.1675, \"Viscera_weight\": 0.081, \"Shell_weight\": 0.135, \"Rings\": 10, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.49, \"Diameter\": 0.38, \"Height\": 0.135, \"Whole_weight\": 0.5415, \"Shucked_weight\": 0.2175, \"Viscera_weight\": 0.095, \"Shell_weight\": 0.19, \"Rings\": 11, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.535, \"Diameter\": 0.405, \"Height\": 0.145, \"Whole_weight\": 0.6845, \"Shucked_weight\": 0.2725, \"Viscera_weight\": 0.171, \"Shell_weight\": 0.205, \"Rings\": 10, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.47, \"Diameter\": 0.355, \"Height\": 0.1, \"Whole_weight\": 0.4755, \"Shucked_weight\": 0.1675, \"Viscera_weight\": 0.0805, \"Shell_weight\": 0.185, \"Rings\": 10, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.5, \"Diameter\": 0.4, \"Height\": 0.13, \"Whole_weight\": 0.6645, \"Shucked_weight\": 0.258, \"Viscera_weight\": 0.133, \"Shell_weight\": 0.24, \"Rings\": 12, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"I\", \"Length\": 0.355, \"Diameter\": 0.28, \"Height\": 0.085, \"Whole_weight\": 0.2905, \"Shucked_weight\": 0.095, \"Viscera_weight\": 0.0395, \"Shell_weight\": 0.115, \"Rings\": 7, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"F\", \"Length\": 0.44, \"Diameter\": 0.34, \"Height\": 0.1, \"Whole_weight\": 0.451, \"Shucked_weight\": 0.188, \"Viscera_weight\": 0.087, \"Shell_weight\": 0.13, \"Rings\": 10, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.365, \"Diameter\": 0.295, \"Height\": 0.08, \"Whole_weight\": 0.2555, \"Shucked_weight\": 0.09699999999999999, \"Viscera_weight\": 0.043, \"Shell_weight\": 0.1, \"Rings\": 7, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.45, \"Diameter\": 0.32, \"Height\": 0.1, \"Whole_weight\": 0.381, \"Shucked_weight\": 0.1705, \"Viscera_weight\": 0.075, \"Shell_weight\": 0.115, \"Rings\": 9, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.355, \"Diameter\": 0.28, \"Height\": 0.095, \"Whole_weight\": 0.2455, \"Shucked_weight\": 0.0955, \"Viscera_weight\": 0.062, \"Shell_weight\": 0.075, \"Rings\": 11, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"I\", \"Length\": 0.38, \"Diameter\": 0.275, \"Height\": 0.1, \"Whole_weight\": 0.2255, \"Shucked_weight\": 0.08, \"Viscera_weight\": 0.049, \"Shell_weight\": 0.085, \"Rings\": 10, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"F\", \"Length\": 0.565, \"Diameter\": 0.44, \"Height\": 0.155, \"Whole_weight\": 0.9395, \"Shucked_weight\": 0.4275, \"Viscera_weight\": 0.214, \"Shell_weight\": 0.27, \"Rings\": 12, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.55, \"Diameter\": 0.415, \"Height\": 0.135, \"Whole_weight\": 0.7635, \"Shucked_weight\": 0.318, \"Viscera_weight\": 0.21, \"Shell_weight\": 0.2, \"Rings\": 9, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.615, \"Diameter\": 0.48, \"Height\": 0.165, \"Whole_weight\": 1.1615, \"Shucked_weight\": 0.513, \"Viscera_weight\": 0.301, \"Shell_weight\": 0.305, \"Rings\": 10, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.56, \"Diameter\": 0.44, \"Height\": 0.14, \"Whole_weight\": 0.9285, \"Shucked_weight\": 0.3825, \"Viscera_weight\": 0.188, \"Shell_weight\": 0.3, \"Rings\": 11, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.58, \"Diameter\": 0.45, \"Height\": 0.185, \"Whole_weight\": 0.9955, \"Shucked_weight\": 0.3945, \"Viscera_weight\": 0.272, \"Shell_weight\": 0.285, \"Rings\": 11, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.59, \"Diameter\": 0.445, \"Height\": 0.14, \"Whole_weight\": 0.9309999999999999, \"Shucked_weight\": 0.35600000000000004, \"Viscera_weight\": 0.23399999999999999, \"Shell_weight\": 0.28, \"Rings\": 12, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.605, \"Diameter\": 0.475, \"Height\": 0.18, \"Whole_weight\": 0.9365, \"Shucked_weight\": 0.39399999999999996, \"Viscera_weight\": 0.21899999999999997, \"Shell_weight\": 0.295, \"Rings\": 15, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.575, \"Diameter\": 0.425, \"Height\": 0.14, \"Whole_weight\": 0.8635, \"Shucked_weight\": 0.39299999999999996, \"Viscera_weight\": 0.22699999999999998, \"Shell_weight\": 0.2, \"Rings\": 11, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.58, \"Diameter\": 0.47, \"Height\": 0.165, \"Whole_weight\": 0.9975, \"Shucked_weight\": 0.3935, \"Viscera_weight\": 0.242, \"Shell_weight\": 0.33, \"Rings\": 10, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.68, \"Diameter\": 0.56, \"Height\": 0.165, \"Whole_weight\": 1.639, \"Shucked_weight\": 0.6055, \"Viscera_weight\": 0.2805, \"Shell_weight\": 0.46, \"Rings\": 15, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.665, \"Diameter\": 0.525, \"Height\": 0.165, \"Whole_weight\": 1.338, \"Shucked_weight\": 0.5515, \"Viscera_weight\": 0.3575, \"Shell_weight\": 0.35, \"Rings\": 18, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.68, \"Diameter\": 0.55, \"Height\": 0.175, \"Whole_weight\": 1.798, \"Shucked_weight\": 0.815, \"Viscera_weight\": 0.3925, \"Shell_weight\": 0.455, \"Rings\": 19, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.705, \"Diameter\": 0.55, \"Height\": 0.2, \"Whole_weight\": 1.7095, \"Shucked_weight\": 0.633, \"Viscera_weight\": 0.4115, \"Shell_weight\": 0.49, \"Rings\": 13, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.465, \"Diameter\": 0.355, \"Height\": 0.105, \"Whole_weight\": 0.4795, \"Shucked_weight\": 0.22699999999999998, \"Viscera_weight\": 0.124, \"Shell_weight\": 0.125, \"Rings\": 8, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.54, \"Diameter\": 0.475, \"Height\": 0.155, \"Whole_weight\": 1.217, \"Shucked_weight\": 0.5305, \"Viscera_weight\": 0.3075, \"Shell_weight\": 0.34, \"Rings\": 16, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.45, \"Diameter\": 0.355, \"Height\": 0.105, \"Whole_weight\": 0.5225, \"Shucked_weight\": 0.237, \"Viscera_weight\": 0.1165, \"Shell_weight\": 0.145, \"Rings\": 8, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.575, \"Diameter\": 0.445, \"Height\": 0.135, \"Whole_weight\": 0.883, \"Shucked_weight\": 0.381, \"Viscera_weight\": 0.2035, \"Shell_weight\": 0.26, \"Rings\": 11, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"M\", \"Length\": 0.355, \"Diameter\": 0.29, \"Height\": 0.09, \"Whole_weight\": 0.3275, \"Shucked_weight\": 0.134, \"Viscera_weight\": 0.086, \"Shell_weight\": 0.09, \"Rings\": 9, \"Male\": 1, \"Female\": 0, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.45, \"Diameter\": 0.335, \"Height\": 0.105, \"Whole_weight\": 0.425, \"Shucked_weight\": 0.1865, \"Viscera_weight\": 0.091, \"Shell_weight\": 0.115, \"Rings\": 9, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"F\", \"Length\": 0.55, \"Diameter\": 0.425, \"Height\": 0.135, \"Whole_weight\": 0.8515, \"Shucked_weight\": 0.36200000000000004, \"Viscera_weight\": 0.196, \"Shell_weight\": 0.27, \"Rings\": 14, \"Male\": 0, \"Female\": 1, \"Infant\": 0}, {\"Sex\": \"I\", \"Length\": 0.24, \"Diameter\": 0.175, \"Height\": 0.045, \"Whole_weight\": 0.07, \"Shucked_weight\": 0.0315, \"Viscera_weight\": 0.0235, \"Shell_weight\": 0.02, \"Rings\": 5, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"I\", \"Length\": 0.205, \"Diameter\": 0.15, \"Height\": 0.055, \"Whole_weight\": 0.042, \"Shucked_weight\": 0.0255, \"Viscera_weight\": 0.015, \"Shell_weight\": 0.012, \"Rings\": 5, \"Male\": 0, \"Female\": 0, \"Infant\": 1}, {\"Sex\": \"I\", \"Length\": 0.21, \"Diameter\": 0.15, 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